New approach to improve soil-water characteristic curve to reduce variation in estimation of unsaturated permeability function
Bibliographic record
Abstract
The unsaturated permeability function is often estimated from the soil-water characteristic curve (SWCC) of a soil. A complete SWCC measurement can improve the estimation of the unsaturated permeability function. In most laboratories, the SWCC can be measured up to a suction of 100 kPa using a Tempe cell. However, complete measurement of the SWCC is an expensive and time-consuming task. Therefore, this paper presents a new approach to estimate SWCC data points beyond 100 kPa suction to complement the SWCC measured up to a suction of 100 kPa. The new SWCC is then used to estimate the unsaturated permeability function. The proposed approach uses knowledge of the grain-size distribution curve and measured SWCC data at 100 kPa suction to estimate the SWCC data points beyond 100 kPa suction. To verify the proposed procedure, SWCC tests were conducted over a wide range of suctions for coarse kaolin and a triaxial permeameter system was used to directly measure unsaturated permeability of the coarse kaolin. The proposed procedure is found to reduce the variation between unsaturated permeability functions estimated by various estimation models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".